The increasing penetration of renewable energy sources (RES) and the development of the cyber-physical microgrids (CPMs) make greater demands for frequency control of microgrids. The common approach for frequency cont...
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The increasing penetration of renewable energy sources (RES) and the development of the cyber-physical microgrids (CPMs) make greater demands for frequency control of microgrids. The common approach for frequency control is controlling the micro-turbine to compensate for frequency deviations, with energy storage systems serving as an auxiliary approach. This article proposes an online adaptive frequency control method to control the governor and energy storage to realize the frequency recovery of microgrid, subject to the external unknown disturbances caused by wind turbine, power load and false data injection (FDI) attacks. First, the non-zero sum (NZS) games of the considered system are modeled in this work, where the unknown disturbances are also taken into account. For the sake of estimating the unknown disturbances, a disturbance observer (DOB) for the microgrid system is introduced. Then, on the basis of the estimated results of the applied DOB, the disturbance compensation input is derived to offset the interference of the unknown disturbance. Meanwhile, the adaptive dynamic programming (ADP) approach is employed to derive the adaptive optimal control input for the NZS games of microgrid system. Besides, the dynamic event-triggered (DET) control is introduced, reducing the occupation of computing resources. By utilizing the Lyapunov's method, the stability of the closed-loop system, the convergence of the estimation weight, the estimation disturbances and the system state are guaranteed. The effectiveness of the proposed method is ultimately verified by the simulation results.
We propose a multi-period multi-product location-inventory model considering an (R, s, S) periodic review policy and modular stochastic capacity constraints. The objective is to define which regional warehouses to ope...
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A dynamic programming algorithm is proposed for parameter extraction of the single-diode model (SDM). Five parameters of SDM are extracted from current-voltage curves of InGaN/GaN multi-quantum wells solar cells under...
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In future power systems dominated by intermittent renewable generation, nuclear power plants will increasingly need to follow uncertain loads. However, regulatory and technical constraints limit the frequency of load-...
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Efficient computation of expected values is paramount in scenario analysis and decision-making, especially for problems involving large finite state machines with complex state dependencies and transitions. Traditiona...
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In this paper, a data-driven fault-tolerant control (FTC) method is proposed to solve the consensus problem of constrained multiagent systems (MASs) with denial-of-service attacks. First, a resilient distributed obser...
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Safe and economic operation of networked systems is challenging. Optimization-based schemes are frequently considered, since they achieve near-optimality while ensuring safety via the explicit consideration of constra...
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This article develops a fully decentralized approximate dynamic programming (FD-ADP) algorithm for stochastic energy management (SEM) of a networked microgrid (NMG) system. First, considering the ac power flow constra...
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This article develops a fully decentralized approximate dynamic programming (FD-ADP) algorithm for stochastic energy management (SEM) of a networked microgrid (NMG) system. First, considering the ac power flow constraints, an alternating direction method of multipliers (ADMM)-based decentralized SEM framework is proposed for NMG coordination. Then, a transactive energy scheme is introduced to further decouple each microgrid (MG) optimization for privacy enhancement and computation reduction. Next, a FD-ADP algorithm is proposed to cope with the real-time uncertainties. The piecewise linear function (PLF) is employed for value function approximation, and a fully decentralized PLF slope update method based on ADMM framework is designed for decentralized property preservation, which trains the value function just through each MG local information and neighboring communication, thus the well-trained decentralized PLF slopes can help achieve the global optimal SEM strategy for NMG coordination under stochastic environments. Finally, case studies demonstrate the effectiveness of the proposed ADMM-based FD-ADP algorithm in terms of decentralized optimization, decentralized training, and global optimality.
This paper presents a novel path planning method for real-time robotic path planning in a dynamic environment involving moving obstacles. It combines on a holistic platform a global approach to rapidly generate initia...
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This paper presents a novel path planning method for real-time robotic path planning in a dynamic environment involving moving obstacles. It combines on a holistic platform a global approach to rapidly generate initial paths of prominent diversity and a heuristic approach to enable local path refinement for enhanced computational efficiency, exploration, and robustness. The global approach innovates a formulation that treats a path planning problem with a visibility graph as a Markov decision process and decomposes the process into many subproblems. A new evolutionary dynamic programming approach (EDP) is proposed to solve these subproblems in an iterative manner using graphics processing unit (GPU) computing to allow backpropagation of state values from goal to start points. The EDP generates multiple feasible initial paths with salient state values, each initializing an independent genetic algorithm (GA) optimization on waypoints only near the mobile robot, and all GAs are run in parallel on GPU, further improving exploration and convergence speed. The strategy to fully utilize CPU/GPU resources for various components in the pipeline is also established. The proposed algorithms are then implemented on an edge computing device (Jetson AGX Xavier) onboard a mobile robot (TurtleBot 3 Waffle Pi). Optimal paths can be continuously generated at the rate of 0.1 seconds/path, enabling successful obstacle avoidance and robot navigation through dynamic environments and, hence, verifying the real-time capabilities and accuracy of the present method. Compared to other benchmarks, the present method greatly enhances path planning robustness, computing speed, and path quality.
The purpose of this note is to extend the Approximate dynamic programming (ADP) method to the infinite time stochastic optimal control (ergodic) problem. It is also shown that a modification of the basic algorithm sol...
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